Biopsy Core

Biopsy core analysis is crucial for accurate cancer diagnosis and treatment planning, with research focusing on improving efficiency and accuracy through digital pathology and machine learning. Current efforts leverage deep learning models, including convolutional neural networks (CNNs) and transformers, to analyze digital images from various biopsy types (e.g., tissue, liquid) and modalities (e.g., brightfield, fluorescence, ultrasound), often incorporating techniques like virtual staining and weakly supervised learning to overcome data limitations. These advancements promise faster, more accurate diagnoses and personalized treatment strategies, ultimately improving patient outcomes and streamlining clinical workflows.